From Vision to Assistance: Gaze and Vision-Enabled Adaptive Control for a Back-Support Exoskeleton

Back-support exoskeletons have been proposed to mitigate spinal loading in industrial handling, yet their effectiveness critically depends on timely and context-aware assistance. Most existing approaches rely either on load-estimation techniques (e.g., EMG, IMU) or on vision systems that do not directly inform control. In this work, we present a vision-gated control framework for an active lumbar occupational exoskeleton that leverages egocentric vision with wearable gaze tracking. The proposed system integrates real-time grasp detection from a first-person YOLO-based perception system, a finite-state machine (FSM) for task progression, and a variable admittance controller to adapt torque delivery to both posture and object state. A user study with 15 participants performing stooping load lifting trials under three conditions (no exoskeleton, exoskeleton without vision, exoskeleton with vision) shows that vision-gated assistance significantly reduces perceived physical demand and improves fluency, trust, and comfort. Quantitative analysis reveals earlier and stronger assistance when vision is enabled, while questionnaire results confirm user preference for the vision-gated mode. These findings highlight the potential of egocentric vision to enhance the responsiveness, ergonomics, safety, and acceptance of back-support exoskeletons.

Survey on Semantic Interpretation of Tabular Data: Challenges and Directions

Tabular data plays a pivotal role in various fields, making it a popular format for data manipulation and exchange, particularly on the web. The interpretation, extraction, and processing of tabular information are invaluable for knowledge-intensive applications. Notably, significant efforts have been invested in annotating tabular data with ontologies and entities from background knowledge graphs, a process known as Semantic Table Interpretation (STI). STI automation aids in building knowledge graphs, enriching data, and enhancing web-based question answering. This survey aims to provide a comprehensive overview of the STI landscape. It starts by categorizing approaches using a taxonomy of 31 attributes, allowing for comparisons and evaluations. It also examines available tools, assessing them based on 12 criteria. Furthermore, the survey offers an in-depth analysis of the Gold Standards used for evaluating STI approaches. Finally, it provides practical guidance to help end-users choose the most suitable approach for their specific tasks while also discussing unresolved issues and suggesting potential future research directions.

Integrating Reinforcement Learning with Foundation Models for Autonomous Robotics: Methods and Perspectives

Foundation models (FMs), large deep learning models pre-trained on vast, unlabeled datasets, exhibit powerful capabilities in understanding complex patterns and generating sophisticated outputs. However, they often struggle to adapt to specific tasks. Reinforcement learning (RL), which allows agents to learn through interaction and feedback, offers a compelling solution. Integrating RL with FMs enables these models to achieve desired outcomes and excel at particular tasks. Additionally, RL can be enhanced by leveraging the reasoning and generalization capabilities of FMs. This synergy is revolutionizing various fields, including robotics. FMs, rich in knowledge and generalization, provide robots with valuable information, while RL facilitates learning and adaptation through real-world interactions. This survey paper comprehensively explores this exciting intersection, examining how these paradigms can be integrated to advance robotic intelligence. We analyze the use of foundation models as action planners, the development of robotics-specific foundation models, and the mutual benefits of combining FMs with RL. Furthermore, we present a taxonomy of integration approaches, including large language models, vision-language models, diffusion models, and transformer-based RL models. We also explore how RL can utilize world representations learned from FMs to enhance robotic task execution. Our survey aims to synthesize current research and highlight key challenges in robotic reasoning and control, particularly in the context of integrating FMs and RL--two rapidly evolving technologies. By doing so, we seek to spark future research and emphasize critical areas that require further investigation to enhance robotics. We provide an updated collection of papers based on our taxonomy, accessible on our open-source project website at: https://github.com/clmoro/Robotics-RL-FMs-Integration.